CN103800075B - The system and method for the specific modeling of patient for hepatic tumor ablation art - Google Patents
The system and method for the specific modeling of patient for hepatic tumor ablation art Download PDFInfo
- Publication number
- CN103800075B CN103800075B CN201310757217.2A CN201310757217A CN103800075B CN 103800075 B CN103800075 B CN 103800075B CN 201310757217 A CN201310757217 A CN 201310757217A CN 103800075 B CN103800075 B CN 103800075B
- Authority
- CN
- China
- Prior art keywords
- target organ
- patient
- simulated
- calculating
- thermal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B23/00—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes
- G09B23/28—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for medicine
- G09B23/30—Anatomical models
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Chemical & Material Sciences (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Educational Technology (AREA)
- Educational Administration (AREA)
- Business, Economics & Management (AREA)
- Medicinal Chemistry (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Biomedical Technology (AREA)
- Surgery (AREA)
- Urology & Nephrology (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The present invention relates to the system and method for the specific modeling of the patient for hepatic tumor ablation art.Disclose the method and system that a kind of patient specific models based on hepatic tumor ablation art carry out the planning of tumour ablation art and guidance.Patient specific anatomic's model of the circulatory system of liver and liver is estimated from the 3D medical images of patient.Blood flow in the circulatory system and liver based on patient specific anatomic's model emulation liver.The blood flow of emulation in venous system and liver based on virtual ablation probe position and liver emulates the thermal diffusion caused by ablation.Meronecrosis in thermal diffusion emulation liver based on emulation.The Visual Graph of the necrotic zone of emulation is generated, and displays it to user for making a policy and carrying out optimum treatment planning and guidance.
Description
This application claims priority to U.S. provisional application No. 61/724,567, filed on 9/11/2012, the disclosure of which is incorporated herein by reference.
Background
The present invention relates to ablation planning and more particularly to therapy planning and guidance based on patient-specific models of liver tumor ablation using medical imaging data.
Ablation is one option for cancer treatment. Despite recent advances in cancer treatment, treatment of abdominal primary and metastatic tumors remains a significant challenge. For example, hepatocellular carcinoma (HCC) is one of the most common malignancies encountered worldwide (e.g., greater than one million cases per year). HCC was seen in 1 of 153 individuals in the united states alone, with a 5-year survival rate of less than 15% reported.
Hepatectomy (partial hepatectomy) is the currently preferred option for patients with localized disease, both primary liver cancer and liver metastases. In the selected case of early HCC, a total liver resection with liver transplantation may also be considered. Unfortunately, less than 25% of patients with primary or secondary liver cancer are candidates for resection or transplantation, primarily due to tumor type, location, or underlying liver disease. Therefore, there is increasing interest in ablative methods for the treatment of unresectable liver tumors. This technique employs complete local in situ tumor destruction rather than ablation. Various methods have been employed to locally ablate tissue. Radiofrequency ablation (RFA) is most commonly used, but other techniques are also used, including ethanol injection, cryotherapy, irreversible electroporation, and microwave ablation.
The RFA procedure is performed by placing the probe in the target region in the soft tissue of the liver, i.e. within the tumor. The electrodes at the tip of the probe generate heat, which is conducted into the surrounding tissue, causing coagulation necrosis at temperatures between 50 ℃ and 100 ℃. In addition to increasing the number of patients who are eligible for the curative conditions of liver cancer among unresectable patients, local tissue ablation has significant benefits because ablation can be performed using minimally invasive methods, including percutaneously and laparoscopically.
To place the probe at the target location, the physician relies on intra-operative imaging techniques, such as ultrasound. However, the success of the process depends on the optimal placement and heat transfer of the probe. Different placements may have different results. The success of ablation is further challenged by hepatic vessels that dissipate heat, thereby potentially reducing RFA efficiency.
Disclosure of Invention
The present invention provides a method and system for patient specific modeling for liver tumor ablation. Embodiments of the present invention model the effects of ablation procedures including heat diffusion, cellular necrosis, and blood flow through blood vessels and the liver. Starting from a pre-operative medical image of a patient, such as a Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) image, a patient-specific geometry of the liver and the venous system is automatically estimated. The vascular structure of the patient is interpreted as a heat sink in the biological heat transfer model. The biological heat transfer model is coupled to a computational fluid dynamics solver to accurately take into account the effect of blood circulation on the dissipated heat. A cell necrosis model is used to simulate cell death due to overheating and the simulated necrotic area can be visualized.
In one embodiment of the invention, a patient-specific anatomical model of the liver and of the hepatic venous system is estimated from 3D medical image data of the patient. The blood flow in the liver and the venous system of the liver is simulated based on the patient-specific anatomical model. Heat diffusion due to ablation is simulated based on the virtual ablation probe position and its operating parameters and simulated blood flow in the liver and venous system of the liver. Cell necrosis in the liver was simulated based on the simulated heat diffusion. A viewable view of the simulated necrotic region and the temperature map is generated.
These and other advantages of the present invention will become apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying drawings.
Drawings
FIG. 1 illustrates a method for patient-specific modeling for liver tumor ablation in accordance with an embodiment of the present invention;
fig. 2 shows exemplary results for estimating a patient-specific anatomical model of a liver;
FIG. 3 illustrates an exemplary computational fluid dynamics (CFID) model of the hepatic venous circulatory system;
FIG. 4 illustrates an algorithm implemented for a computational model for simulating radio frequency ablation (ABA) according to an embodiment of the present invention;
FIG. 5 illustrates an exemplary visual view of a simulated necrotic region;
FIG. 6 illustrates an exemplary temperature map;
FIG. 7 illustrates an analysis of spatial and temporal simulation results compared to an analytical solution; and
FIG. 8 is a high level block diagram of a computer capable of implementing the present invention.
Detailed Description
The invention relates to patient specific modeling and simulation of liver tumor ablation using medical imaging data. Embodiments of the present invention are described herein to give a visual understanding of methods of patient-specific modeling and simulation using medical imaging data. Digital images are typically made up of a digital representation of one or more objects (or shapes). Digital representations of objects are described herein generally in terms of identifying and manipulating objects. Such manipulations are virtual manipulations done in the memory or other circuitry/hardware of a computer system. Thus, it is to be understood that embodiments of the present invention may be executed within a computer system using data stored within the computer system.
Embodiments of the present invention utilize a computational framework for patient-specific planning and guidance of radiofrequency ablation procedures. Starting from pre-operative 3D medical images, such as Computed Tomography (CT) images, the geometry of the liver and venous system is automatically estimated using efficient machine algorithms. The bio-thermal equation is then solved to obtain a temperature profile of the whole liver over time. The tissue parameters are updated at each time step calculated according to the cell necrosis model.
Fig. 1 illustrates a method of patient-specific modeling for liver tumor ablation according to an embodiment of the present invention. The method of fig. 1 transforms medical image data representative of a patient's liver anatomy to provide a patient-specific simulation of liver tumor ablation. It should be understood that the method of fig. 1 is not limited to the liver and may be similarly applied to other target organs as well. At step 102, pre-operative 3D medical image data of at least a liver region of a patient is received. The pre-operative 3D image data may be acquired using any type of medical imaging modality, such as Computed Tomography (CT), three-dimensional rotational angiography, magnetic Resonance Imaging (MRI), ultrasound (US), positron Emission Tomography (PET), etc., assuming that the liver is fully visible in the medical image data. The medical image data may be received directly from an image acquisition device, such as a CT scanner, C-arm image acquisition device, MRI scanner, or US scanner, or the pre-operative cardiac image data may be received by loading previously stored cardiac image data of the patient. In a possible embodiment, a computed tomography, fluoroscopy, x-ray or CT angiography system may be used to acquire the medical image data. The patient may ingest or be injected with a contrast agent that is substantially opaque to x-rays. The contrast agent accumulates in or is located in the circulatory system, and thus the blood vessels contrast with the tissue. The pre-operative 3D medical image data may be generated using any scan sequence or method, such as a CT angiography mode or rotational angiography.
At step 104, an indication of the position of the virtual ablation probe and its operating parameters is received. In one embodiment, a user indication of a placement location of an ablation probe is received. Ablation will occur in or near a lesion (e.g., tumor) or other tissue region. Given the anatomy, type of ablation procedure, type of ablation device, or other constraints, placement in tissue may be limited. The user may indicate possible placement of the ablation probe by selecting one or more locations in the pre-operative medical image data. For example, the user may select a location in the multi-planar reconstruction of the patient using an input device such as a mouse, touch screen, or the like.
In possible embodiments, multiple locations of the ablation probe may be provided. For example, a user may indicate a placement sequence for simulating successive ablation operations or applications. In this case, the sequence is simulated by repeating steps 108-114 of FIG. 1 for each sequential probe position using the results from the previous run. By using the modeling of cell necrosis in step 112, the changed tissue properties can be taken into account for different locations during subsequent runs. Sequential placement can be used for larger tumors where single probe placement does not provide sufficient thermal dose coverage of the tumor. In another possible embodiment, the user may indicate multiple placements in order to simultaneously simulate ablation using multiple devices. The accumulated thermal dose was calculated based on a single round of simulation.
In an alternative embodiment, the user does not indicate placement. Instead, the location is automatically selected based on the image data, such as by identifying the center of the tumor. The various possible placements may be automatically identified and verified with separate simulations.
In addition to the location of the virtual ablation probe, the user may also input the spatial extent of the ablation probe, the type of ablation, the duration, the desired dose, an indication of the spatial extent of the tumor, an indication of the location in the tumor, the amount of energy used for the ablation, the type of ablation device, the energy sequence, and/or other characteristics of the ablation or tissue. The various inputs may be automated. Instead of user input, the processor provides this information.
In step 106, a patient-specific anatomical model of the liver and of the circulatory system in the liver is estimated from the 3D medical image data. The patient-specific anatomical model is a detailed anatomical model of the patient's soft tissue of the liver, tumor, hepatic vein, portal vein, and artery. The 3D surface of the liver may be automatically segmented from the 3D medical image data. In one embodiment, the patient-specific anatomical model is semi-automatically estimated from the CT data using graph-theory methods such as random walk segmentation (random walk segmentation). For each structure (soft tissue, tumor, hepatic vein, portal vein, artery), the user defines seeds inside and outside the region of interest. The stochastic Woke algorithm then automatically estimates the boundaries of the structure. The process can be interactively improved by the user if necessary. The resulting segments are then merged into a multi-label mask image, which is then used to generate a tetrahedral multi-domain mesh.
Fig. 2 shows exemplary results for estimating a patient-specific anatomical model of the liver. As shown in fig. 2, the image 200 shows the liver segment 102 overlaid on a CT image. Image 210 shows a tetrahedral volume mesh generated from a liver segmentation, including soft tissue 212, hepatic veins 214, portal veins 216, and a segmented tumor 218.
Returning to fig. 1, at step 108, the blood flow in the liver is simulated based on the patient-specific anatomical model of the liver and venous system. The blood flow through the venous system of the liver acts as a heat sink when spreading the heat applied by the ablation probe. Blood flow in a patient-specific anatomical model is simulated to provide personalized modeling of the heat sink due to blood flow. The location of the blood vessel in the tissue region, the size of the blood vessel, and/or other blood vessel characteristics are used to model the thermal radiator characteristics in the region of interest. The characteristic may be extracted from the segmented vessel information in a patient-specific anatomical model of the liver.
The liver was treated as a porous medium, so solving Darcy's law provides a velocity field throughout the organ, which is used in the advection part of the heat transfer model. The blood velocity v inside the soft tissue is calculated according to Darcy's law: v = -k/(μ e) 2／3 ) del.p, where p is the pressure within the soft tissue and μ is the dynamic viscosity of the blood flow. This is equivalent to solving the Laplace equation del 2／3 ) del.p) =0. At the boundary of the liver, due toNo or little flow leaks out of the liver, so the nemann (Neumann) boundary condition is used, whereas at the portal vein and hepatic vein endings the Dirichlet (Dirichlet) boundary condition is applied. Since pressure cannot be estimated in vivo, a Computational Fluid Dynamics (CFD) model of the hepatic venous circulation system is used to estimate pressure. Fig. 3 illustrates an exemplary CFD model of the hepatic venous circulation system 300. As shown in fig. 3, the arrows represent blood flow, the circles represent portal vein endings, and the squares represent hepatic blood vessel endings. Order toAs a result of the vena cava inflow,as portal vein inflow, andas the vena cava outflow, the vena cava outflow equals due to conservation of massThe hepatic artery may be ignored or included as well. Assuming that there is a small pressure (e.g. p) at the vena cava outlet 0 =1mmHg)。
The blood flow and pressure distribution is calculated within the vena cava (302 in fig. 3) using three-dimensional computational fluid dynamics, for example, using the Navier-Stokes equation with an unstable incompressible viscous term. Blood was modeled as having a pre-specified density (e.g., density =1.05 g/cm) 3 ) And newtonian fluids of viscosity (e.g., viscosity =0.004Pa s). The parameters may vary from patient to patient. The plug profile velocity field is applied at the entrance (square in FIG. 3) according to the outflowAnd the cross-sectional area of each inlet is calculated. Providing downstream pressure p for each entry of the vena cava by computational fluid dynamics i - . Each portal vein ending (circle in fig. 3)) Upstream pressure p of + Is assumed to be constant so that the calculated perfusion flow through the vena cava inlet matches the three-dimensional computational fluid dynamics inlet flow profile. Once p is estimated + The blood flow in the portal vein is calculated using a three-dimensional computational fluid dynamics solver (304 in figure 3). The effect of heat on the flow viscosity can be neglected. Thus, flow-related calculations to simulate blood flow in the liver may be performed prior to the simulation of heat diffusion.
Returning to fig. 1, at step 110, heat diffusion in the liver due to the ablation procedure is simulated based on the simulated blood flow and the position of the virtual ablation probe. The diffusion of heat in liver tissue over time is simulated by calculating the progression or diffusion of temperature changes over time. The temperature distribution field is solved as a function of time using partial differential equations or other equations. The numerical solution to the biological heat transfer equation produces an underlying temperature distribution field that varies in space and time. Calculating the heat diffusion in biological tissue amounts to solving coupled biological thermal equations derived from the porous media theory, where each elementary volume is assumed to include a tissue portion and a blood portion. The two main simplifications used to solve the coupled biothermal equation are the Pennes model and the Wulff-Klinger (WK) model.
In the Pennes model, the blood temperature is assumed to be constant, which is true in the case of the approach to large blood vessels, where the blood velocity is high. The Pennes model can be expressed as:
(1-ε)p t c t (δT t ／δt)=(1-ε)Q+(1-ε)del.(d t del.T t )+H(T b0 -T t )。 (1)
in the Wulff-Klinger model, an equilibrium between tissue temperature and vascular temperature is assumed (Tt = Tb). The model is therefore well suited for small blood vessels where blood velocity is low. The Wulff-Klinger model can be expressed as:
(1-ε)p t c t (δT t ／δt)=(1-ε)Q+(1-ε)del.(d t del.T t )-εp b c b v.del.T t 。 (2)
in both equations, T is the temperature, Q is the source term, and v is the blood velocity. Subscripts t and b refer to the tissue and blood aspects, respectively. T is b0 Is the average temperature of the blood in the portal vein and hepatic vein, which is assumed to be constant. Definitions and exemplary values for the remaining model parameters are provided in table 1 below.
TABLE 1 parameter values used in the simulation
The main difference between the Pennes and Wulff-Klinger models is their cooling term (i.e. the last term on the right). The former acts as a volume uniform heat sink, while the latter explains the directional effect of blood flow on the tissue temperature field. Thus, both equations can be easily implemented in a modular fashion using one or the other cooling term to account for tissue inhomogeneities.
Current imaging techniques may not allow an accurate ratio between blood and liver tissue to be estimated. Large blood vessels are clearly identified in the patient-specific anatomical model of the liver, but small capillary vessels are difficult to image. Embodiments of the present invention therefore solve the biothermal equation by combining the Pennes and Wulff-Klinger models in a unified and modular framework to model large and small blood vessels. Assuming that the vessels and surrounding tissue are isolated from each other, the diffusion equation p is used at each place in the tetrahedral domain (i.e., each place in the domain of the patient-specific anatomical model) t c t (δT t ／δt)＝Q+del.(d t Tt) to find the liver temperature T t . The addition of the cooling term H (Tb 0-Tt)/(1-epsilon) when the tetrahedron belongs to a large vessel or artery (Pennes model), and the addition of the cooling term ε p when the tetrahedron belongs to a small vessel or soft tissue (Wulff-Klinger model) b c b v.del.T t 。
The bio-thermal and porous media models were solved using Finite Element Method (FEM). A weak form of the bio-thermal equation is discretized using, for example, a test function defined on linear tetrahedral elements (e.g., garlekin's method). Thus, the heat diffusion is obtained by solving a linear system at each time step. Efficient hidden formats can be used to obtain unconditional digital stability, enabling large time steps for increased computational efficiency. A primary condition of FEM is the availability of detailed 3D tetrahedrons, other meshes or samples of the patient's liver. Once the anatomical model is generated, a virtual probe can be placed and ablation can be simulated using FEM.
In one example implementation of the finite element method on a tetrahedral mesh, the equation is discretized:is solved, where U is the temperature, M is the mass matrix, K is the stiffness matrix for the diffusion, reaction and convection sections, and B is the vector including the boundary conditions. The diffusion, reaction and convection terms are implemented in the form of modules, but they may be combined. Newman boundary conditions are used at organ boundaries. The heat source term can be modeled by a Dirichlet boundary condition of 100 ℃ at the virtual probe location. The blood flow through the liver was considered to be a constant temperature of 37 ℃. For the convection term, the Galerkin scheme is characterized three-dimensionally to obtain digital stability. First order implicit Euler (Euler) time discretization is employed. The resulting matrix system Ax = B is solved by using a conjugate gradient iterative algorithm. For computational fluid dynamics, a full 3D naval-stokes viscosity solver expressed in an euler framework using a horizontal set representation of segmented vessels to embed domain boundaries is used.
Returning to fig. 1, at step 112, cellular necrosis in the liver is simulated based on the simulated thermal diffusion. Heat from the ablation probe can cause cell necrosis in the liver. In one embodiment, the tissue damage is modeled using three state models. The model is based on the equation of stateCalculating the change of the concentration of the liver cells (A), the vulnerable cells (C) and the dead cells (D) with time, wherein k f And k b Respectively cell damageThe rate of injury and recovery. In particular, kf is given by the equation:dependent on temperature, where k f - Is a scaling constant, and T k Is a parameter that sets the rate of exponential increase in length with temperature. This equation results in three simultaneous ordinary differential equations that are solved at each node of the computational domain, resulting in a spatially varying cell state field. Any time integration scheme (e.g., first order euler explicit or first order euler implicit) may be used. In an exemplary implementation, the initial conditions may be selected as a =0.99, v =0.01 and D =0.00. Parameter k b ，k f - And T k Exemplary values of (a) are shown in table 1 above. In addition, dead or damaged cells do not have the same heat capacity c as living cells t . This phenomenon can affect the heat transfer and the degree of ablation. Accordingly, the state of the cells is updated at each point in the patient-specific anatomical model of the liver at each time step calculated, given the current temperature of the cells. If healthy or vulnerable, dead cells have a fixed heat capacity c t * If so, the corresponding heat capacity ct is used.
Other cell death models may also be used. For example, in another embodiment, tissue damage may be modeled using a survival score index and an Arrhenius (Arrhenius) class equation. Tissue damage models are experimentally determined, extracted from studies, or theoretically created. The survival score index indicates the amount of live cells compared to dead cells in the area as a function of temperature. The survival score index is calculated from the current temperature using an arrhenius-like model. More precisely, the survival score index is the ratio of viable cells to the total number of cells in the region of interest. Cells in this spatial region die when the survival score index is < < 1. The arrhenius equation models the survival fraction versus temperature.
Figure 4 illustrates an algorithm for implementing a computational model for simulated radio frequency ablation (ABA) according to one embodiment of the present invention. FIG. 4 is a schematic view ofAn algorithm may be used to implement steps 106-112 of fig. 1. As shown in fig. 4, at step 402, a patient-specific model of the liver anatomy is estimated. At 404, an inlet pressure p in the hepatic vein is calculated - i And 3D blood flowAt 406, by fitting Darcy model to hepatic vein downstream pressure p - i And flow ofTo calculate the portal upstream pressure p + . At 408, 3D blood flow in the portal vein is calculated. The blood flow and pressure in the hepatic and portal veins provide the dirichlet boundary conditions for calculating the blood velocity inside the soft tissue. Once blood flow is simulated throughout the patient-specific model of the liver, when t<t end Operations 410 and 412 are performed at each time step (t). At 410, the temperature T is updated using the Pennes model in the great vessels and the Wulff-Klinger model at other locations (e.g., soft tissue). At 412, the cell state is updated based on the simulated temperature by using the cell necrosis model.
Returning to FIG. 1, at step 114, a visualization and temperature map of the simulated necrotic area is output. For example, the temperature map and the simulated necrotic area viewable can be output by displaying the temperature map and the simulated necrotic area viewable on a display of a computer system.
The proposed framework has been evaluated with respect to one patient-specific geometry extracted from pre-operative CT images. A virtual probe is placed in the middle of the tumor. Cells within the sphere around the probe tip were heated at 100 ℃ to mimic the topology of the probe. The protocol used clinically is simulated as: heating was stopped for 10 minutes, followed by 5 minutes, and the process was repeated by growing the sphere range from 10mm to 20mm and finally to 30mm in diameter. A Pennes model is used, which is able to predict the cooling effect of the large veins. The distribution of the return temperature over the liver and the simulated necrotic area. Fig. 5 shows an exemplary visual view of a simulated necrotic area. As shown in fig. 5, the simulated necrotic region 502 is visualized by visualizing all parts of the tetrahedral mesh with dead cell states. Alternatively, the necrotic region may be visualized using an isocontour of the survival score index. Live cell necrosis regions from post-operative MRI were overlaid 504 for comparison. As can be seen in fig. 5, the predicted necrosis level from the simulated necrotic region 502 qualitatively corresponds to the live cell necrosis 504 actually observed on the patient.
The temperature map shows the spatial and temporal variation of temperature in the liver over time. In one embodiment, the liver temperature may be visualized using isothermal contour lines and color maps indicating the heat of a particular region in the liver. Fig. 6 illustrates an exemplary temperature map. As shown in fig. 6, a temperature map 600 showing the temperature at time steps in the simulation is mapped to the CT sectional image.
The inventors evaluated a computational model for simulating radiofrequency ablation in the liver by comparing its characteristics on a conventional tetrahedral beam mesh (tetrahedral beam mesh) on which a 3D analytic solution of instantaneous point sources of mass M at position p 0 =(x 0 ，y 0 ，z 0 ) And time t 0 Is issued, where v = (u, v, w). F, convection-diffusion equationIt is known that:
the following values were used: m (. Degree. C./mm) 3 )＝100，d(mm 2 ／s)＝100，t 0 =0s, and v (mm/s) = (0.001,0,0) (values of the same order of magnitude on patient simulation). The source item is placed at the center of the grid to minimize the impact of boundary conditions. At time t =0s, the temperature values are initialized at each vertex to an analytical solution at time t =0.05s, and the temperatures are analyzed at several points of the grid. FIG. 7 showsAn analysis of the spatial and temporal convergence of the simulation results compared to the analytical solution is made. Image 700 shows a spatial convergence analysis comparing the temperature values of analytical solution 702 over time with temperature values calculated using simulations with grid resolutions 2mm (704), 5mm (706), and 10mm (708). Image 710 illustrates a time convergence analysis comparing the temperature values over time for the analytical solution 712 with temperature values calculated using simulations with time steps 0.005s (714), 0.05s (716), and 0.5s (718). As expected, the more accurate the time step and spatial resolution of the grid are, the more accurate the simulation is compared to the analytical solution. As shown in fig. 7, the time step of 0.5s and the resolution of 5mm provide a good compromise between accuracy and computational cost.
Embodiments of the present invention provide a patient specific model for simulating liver tumor ablation. Embodiments of the present invention simulate heat propagation and cellular necrosis based on a patient-specific anatomical model estimated from medical image data of a patient, and take into account the heat sink effect of the vascular and fenestrated circulation in the liver. Embodiments of the present invention provide a visual view of temperature distribution and therefore lesion propagation and cell necrosis, enabling improved planning and guidance of liver tumor ablation procedures.
In a possible implementation, the optimal probe position may be automatically determined using the simulation framework described above. Temperature spread and corresponding necrosis associated with different locations and/or other deviations in the ablation procedure may be compared to the region to be treated, rather than relying on user feedback. For example, the location, energy levels and sequence of applications that result in maximal, adequate or complete coverage of the tumor and minimal necrosis of healthy tissue can be found by simulating different combinations. By sequentially calculating the thermal dose placed for multiple needles, the placement with the greatest coverage of the tumor can be found. The combination for maximum tumor thermal dose coverage is found in successive optimization cycles for solving for probe position, orientation, and/or other characteristics. Thermal dose coverage may be measured as an area defined by a temperature above a threshold (e.g., 70 degrees celsius). Other measures may be used, such as weighting the necrosis of the tumor most strongly while taking into account weighting measures that avoid necrosis outside the tumor.
The above-described methods for patient-specific modeling and simulation of liver tumor ablation may be implemented on a computer using well-known computer processors, memory units, storage devices, computer software, and other components. A high-level block diagram of such a computer is shown in fig. 8. The computer 802 contains a processor 804, the processor 804 controlling the overall operation of the computer 802 by executing computer program instructions defining such operations. The computer program instructions may be stored in storage device 812 (e.g., magnetic disk) and loaded into memory 810 when execution of the computer program instructions is desired. Thus, the steps of the methods of fig. 1 and 4 may be defined by computer program instructions stored in memory 810 and/or storage 812, and controlled by processor 804 executing the computer program instructions. An image acquisition device 820, such as a CT scanning device, C-arm image acquisition device, MR scanning device, ultrasound device, etc., can be connected to the computer 802 to input image data to the computer 802. It is possible to implement the image acquisition device 820 and the computer 802 as one device. It is also possible for the image capture device 820 and the computer 802 to communicate wirelessly over a network. The computer 802 also includes one or more network interfaces 806 for communicating with other devices via a network. The computer 802 also includes other input/output devices 808 that enable user interaction with the computer 802 (e.g., display, keyboard, mouse, speakers, buttons, etc.). Such an input/output device 808 may be used in conjunction with a set of computer programs as an annotation tool to annotate the quantities received from the image acquisition device 820. Those skilled in the art will appreciate that embodiments of an actual computer may also contain other components, and that FIG. 8 is a high-level diagram of some of the components of such a computer for illustrative purposes.
The foregoing detailed description is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the detailed description, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are merely illustrative of the principles of this invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Various other combinations of features may be implemented by those skilled in the art without departing from the scope and spirit of the invention.
Claims (30)
1. A method of planning and guiding a tumor ablation therapy in a target organ based on a patient-specific model, comprising:
estimating a patient-specific anatomical model of the target organ and a circulatory system of the target organ from medical image data of a patient;
simulating blood flow in a fenestrated region and a blood vessel of the target organ based on the patient-specific anatomical model;
simulating heat spread due to ablation based on the virtual ablation probe position and operating parameters and simulated blood flow in the fenestrated region of the target organ and the blood vessel, taking into account cooling effects from both blood flow in the blood vessel and fenestrated tissue blood perfusion;
simulating cell necrosis in the target organ based on the simulated thermal diffusion; and
generating a viewable view of the patient-specific anatomical model with simulated necrotic regions resulting from simulating the cellular necrosis;
wherein simulating heat diffusion due to ablation based on the virtual ablation probe position and operating parameters and simulated blood flow in the fenestrated region of the target organ and the blood vessel, taking into account cooling effects from both blood flow in the blood vessel and fenestrated tissue blood perfusion, comprises:
for each of a plurality of points in the patient-specific anatomical model:
the temperature is calculated at each of a plurality of time steps using a first bio-thermal equation having a first cooling term if the point belongs to a blood vessel or artery and using a second bio-thermal equation having a second cooling term if the point belongs to a porous medium.
2. The method of claim 1, wherein estimating the target organ and the patient-specific anatomical model of the circulatory system of the target organ from medical image data of the patient comprises:
a patient-specific anatomical model of a liver comprising liver soft tissue, at least one tumor, a hepatic vein, a portal vein, and at least one artery is estimated.
3. The method of claim 2, wherein estimating the patient-specific anatomical model of the liver comprising the soft liver tissue, the at least one tumor, the hepatic vein, the portal vein, and the at least one artery comprises:
separately segmenting each of the soft liver tissue, the at least one tumor, the hepatic vein, the portal vein, and the at least one artery in the medical image using random walk segmentation;
merging the segmented liver soft tissue, the at least one tumor, hepatic vein, portal vein, and the at least one artery into a multi-labeled mask image; and
generating a tetrahedral mesh based on the multi-label mask image.
4. The method of claim 2, wherein simulating blood flow in a fenestrated region and a blood vessel of a target organ based on the patient-specific anatomical model comprises:
calculating 3D blood flow and inlet pressure in the hepatic vein over a plurality of time steps using a Computational Fluid Dynamics (CFD) simulation;
calculating portal vein and artery upstream pressures over a plurality of time steps based on the 3D blood flow and inlet pressure in the hepatic vein; and
calculating 3D blood flow in the portal vein and the at least one artery over the plurality of time steps based on the portal vein upstream pressure using CFD simulation.
5. The method of claim 1, wherein the first bio-thermal equation is a Pennes-type model bio-thermal equation and the second bio-thermal equation is a Wulff-Klinger-type model bio-thermal equation.
6. The method of claim 1, wherein calculating the temperature at each of the plurality of time steps using a first bio-thermal equation having a first cooling term if the point belongs to a blood vessel or artery and using a second bio-thermal equation having a second cooling term if the point belongs to a porous medium comprises:
if the point belongs to a vessel or artery, calculating the temperature at each of the plurality of time steps by solving the following equation:
and
if the point belongs to a porous medium, calculating the temperature at each of the plurality of time steps by solving the following equation:
wherein T is t Is the temperature at time step T, Q is a source term representing the amount of heat applied by the virtual ablation probe at the virtual ablation probe position and operating parameters, v is the blood velocity determined by the simulated blood flow, T b0 Is the mean temperature of the blood in the blood vessels and arteries, p t And p b Tissue and blood density, respectively, c t And c b Heat capacity of tissue and blood, respectively, d t Is the tissue thermal conductivity, H is the convective transfer coefficient, and e is the blood volume fraction.
7. The method of claim 1, wherein the patient-specific anatomical model of the liver comprises soft liver tissue, at least one tumor, a hepatic vein, a portal vein, and at least one artery, and calculating the temperature at each of a plurality of time steps using a first bio-thermal equation having a first cooling term if the point belongs to a blood vessel or an artery and using a second bio-thermal equation having a second cooling term if the point belongs to a porous medium comprises:
calculating the temperature at each of the plurality of time steps using the first bio-thermal equation if the point belongs to a hepatic vein, a portal vein, or the at least one artery; and
if the point belongs to soft liver tissue, calculating the temperature at each of the plurality of time steps using the second bio-thermal equation.
8. The method of claim 1, wherein a thermal diffusion map resulting from simulating the thermal diffusion is adjusted such that temperatures at a plurality of locations in the thermal diffusion map over time match temperatures actually acquired at corresponding locations.
9. The method of claim 1, wherein the thermal diffusion is simulated at each of a plurality of time steps, and simulating cellular necrosis in the target organ based on the simulated thermal diffusion comprises:
calculating changes in the concentrations of live, vulnerable, and dead cells in the target organ over the plurality of time steps based on the simulated thermal diffusion.
10. The method of claim 9, wherein calculating changes in the concentrations of live, vulnerable, and dead cells in the target organ over the plurality of time steps based on the simulated thermal diffusion comprises:
at each time step:
calculating a cell damage rate based on the temperature generated by the simulated thermal diffusion at the time step; and
calculating concentrations of live, vulnerable, and dead cells in the target organ based on the cell damage rate, cell recovery rate, and previous concentrations of live, vulnerable, and dead cells in the target organ.
11. The method of claim 10, wherein simulating cellular necrosis in the target organ based on the simulated thermal diffusion further comprises:
at each time step, updating at least one tissue parameter of a biological thermal model used to simulate the thermal diffusion based on the concentrations of live, vulnerable, and dead cells in the target organ calculated at that time step before simulating the thermal diffusion at the next time step.
12. The method of claim 1, further comprising:
generating a viewable view of a temperature map showing the simulated heat diffusion in the target organ.
13. An apparatus for planning and guiding a tumor ablation procedure in a target organ based on a patient-specific model, comprising:
means for estimating a patient-specific anatomical model of the target organ and the circulatory system of the target organ from medical image data of a patient;
means for simulating blood flow in a fenestrated region and a blood vessel of the target organ based on the patient-specific anatomical model;
means for simulating heat diffusion due to ablation based on the virtual ablation probe position and operating parameters and simulated blood flow in the fenestrated region of the target organ and the blood vessel, taking into account cooling effects from both blood flow in the blood vessel and fenestrated tissue blood perfusion;
means for simulating cell necrosis in the target organ based on the simulated thermal diffusion; and
means for generating a viewable view of the patient-specific anatomical model having a simulated necrotic region resulting from simulating the cellular necrosis;
wherein the means for simulating heat spread due to ablation based on the virtual ablation probe position and operating parameters and simulated blood flow in the fenestrated region of the target organ and the blood vessel taking into account cooling effects from both blood flow in the blood vessel and fenestrated tissue blood perfusion comprises:
means for calculating a temperature for each of a plurality of points in the patient-specific anatomical model at each of a plurality of time steps, the means using a first bio-thermal equation having a first cooling term if the point belongs to a blood vessel or artery and a second bio-thermal equation having a second cooling term if the point belongs to a porous medium.
14. The apparatus of claim 13, wherein the means for estimating a patient-specific anatomical model of the target organ and the circulatory system of the target organ from medical image data of a patient comprises:
means for estimating a patient-specific anatomical model of a liver comprising liver soft tissue, at least one tumor, hepatic veins, portal veins, and at least one artery.
15. The apparatus of claim 14, wherein the means for simulating blood flow in the fenestrated region and the blood vessels of the target organ based on the patient-specific anatomical model comprises:
means for calculating 3D blood flow and inlet pressure in the hepatic vein over a plurality of time steps using a Computational Fluid Dynamics (CFD) simulation;
means for calculating portal vein and artery upstream pressures over a plurality of time steps based on the 3D blood flow and inlet pressure in the hepatic vein; and
means for calculating 3D blood flow in the portal vein and the at least one artery over the plurality of time steps based on the portal vein upstream pressure using CFD simulation.
16. The apparatus of claim 13, wherein the first biofever equation is a Pennes-type model biofever equation and the second biofever equation is a Wulff-Klinger-type model biofever equation.
17. The apparatus of claim 13, wherein the thermal diffusion is simulated at each of a plurality of time steps, and the means for simulating cell necrosis in the target organ based on the simulated thermal diffusion comprises:
means for calculating a change in a concentration of live cells, vulnerable cells, and dead cells in the target organ over the plurality of time steps based on the simulated thermal diffusion.
18. The apparatus of claim 17, wherein the means for calculating changes in the concentrations of live, vulnerable, and dead cells in the target organ over the plurality of time steps based on the simulated thermal diffusion comprises:
means for calculating a cell damage rate at each time step, the means calculating the cell damage rate based on the temperature generated by the simulated thermal diffusion at that time step; and
means for calculating a concentration of live cells, vulnerable cells, and dead cells in the target organ at each time step based on the cell damage rate, cell recovery rate, and previous concentrations of live cells, vulnerable cells, and dead cells in the target organ.
19. The apparatus of claim 18, wherein the means for simulating cell necrosis in the target organ based on the simulated thermal diffusion further comprises:
means for updating at least one tissue parameter of a biological thermal model used to simulate the thermal diffusion based on the calculated concentrations of live, vulnerable, and dead cells in the target organ at each time step before simulating the thermal diffusion at a next time step.
20. The apparatus of claim 13, further comprising:
means for generating a viewable view of a temperature map showing the simulated heat diffusion in the target organ.
21. A non-transitory computer readable medium storing computer program instructions for planning and guiding a tumor ablation procedure in a target organ based on a patient-specific model, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
estimating a patient-specific anatomical model of the target organ and a circulatory system of the target organ from medical image data of a patient;
simulating blood flow in a fenestrated region and a blood vessel of the target organ based on the patient-specific anatomical model;
simulating heat spread due to ablation based on the virtual ablation probe position and operating parameters and simulated blood flow in the fenestrated region of the target organ and the blood vessel, taking into account cooling effects from both blood flow in the blood vessel and fenestrated tissue blood perfusion;
simulating cell necrosis in the target organ based on the simulated thermal diffusion; and
generating a viewable view of the patient-specific anatomical model with simulated necrotic regions resulting from simulating the cellular necrosis;
wherein simulating heat diffusion due to ablation based on the virtual ablation probe position and operating parameters and simulated blood flow in the fenestrated region of the target organ and the blood vessel, taking into account cooling effects from both blood flow in the blood vessel and fenestrated tissue blood perfusion, comprises:
for each of a plurality of points in the patient-specific anatomical model:
the temperature is calculated at each of a plurality of time steps using a first bio-thermal equation having a first cooling term if the point belongs to a blood vessel or artery and using a second bio-thermal equation having a second cooling term if the point belongs to a porous medium.
22. The non-transitory computer readable medium of claim 21, wherein estimating the target organ and the patient-specific anatomical model of the circulatory system of the target organ from medical image data of the patient comprises:
a patient-specific anatomical model of a liver comprising liver soft tissue, at least one tumor, hepatic veins, portal veins, and at least one artery is estimated.
23. The non-transitory computer readable medium of claim 22, wherein simulating blood flow in a fenestrated region and a blood vessel of a target organ based on the patient-specific anatomical model comprises:
calculating 3D blood flow and inlet pressure in the hepatic vein over a plurality of time steps using a Computational Fluid Dynamics (CFD) simulation;
calculating portal vein and artery upstream pressures over a plurality of time steps based on the 3D blood flow and inlet pressure in the hepatic vein; and
calculating 3D blood flow in the portal vein and the at least one artery over the plurality of time steps based on the portal vein upstream pressure using CFD simulation.
24. The non-transitory computer readable medium of claim 21, wherein the first bio-thermal equation is a Pennes-type model bio-thermal equation and the second bio-thermal equation is a Wulff-Klinger-type model bio-thermal equation.
25. The non-transitory computer readable medium of claim 21, wherein calculating the temperature at each of the plurality of time steps using a first bio-thermal equation having a first cooling term if the point belongs to a blood vessel or artery and using a second bio-thermal equation having a second cooling term if the point belongs to a porous medium comprises:
if the point belongs to a vessel or artery, calculating the temperature at each of the plurality of time steps by solving the following equation:
and
if the point belongs to a porous media, calculating the temperature at each of the plurality of time steps by solving the following equation:
wherein T is t Is the temperature at time step T, Q is a source term representing the amount of heat applied by the virtual ablation probe at the virtual ablation probe position and operating parameters, v is the blood velocity determined by the simulated blood flow, T b0 Is the mean temperature of the blood in the blood vessels and arteries, p t And p b Tissue and blood density, respectively, c t And c b Heat capacity of tissue and blood, respectively, d t Is the tissue thermal conductivity, H is the convective transfer coefficient, and e is the blood volume fraction.
26. The non-transitory computer readable medium of claim 21, wherein the patient-specific anatomical model of the target organ is a patient-specific anatomical model of a liver, including soft liver tissue, at least one tumor, a hepatic vein, a portal vein, and at least one artery, and calculating a temperature at each of a plurality of time steps using a first bio-thermal equation having a first cooling term if the point belongs to a blood vessel or an artery and using a second bio-thermal equation having a second cooling term if the point belongs to a porous medium comprises:
calculating the temperature at each of the plurality of time steps using the first bio-thermal equation if the point belongs to a hepatic vein, a portal vein, or the at least one artery; and
if the point belongs to soft liver tissue, calculating the temperature at each of the plurality of time steps using the second bio-thermal equation.
27. The non-transitory computer readable medium of claim 21, wherein the thermal diffusion is simulated at each of a plurality of time steps, and simulating cellular necrosis in the target organ based on the simulated thermal diffusion comprises:
calculating a change in a concentration of live cells, vulnerable cells, and dead cells in the target organ over the plurality of time steps based on the simulated thermal diffusion.
28. The non-transitory computer readable medium of claim 27, wherein calculating changes in the concentrations of live, vulnerable, and dead cells in the target organ over the plurality of time steps based on the simulated thermal diffusion comprises:
at each time step:
calculating a cell damage rate based on the temperature generated by the simulated thermal diffusion at the time step; and
calculating concentrations of live, vulnerable, and dead cells in the target organ based on the cell damage rate, cell recovery rate, and previous concentrations of live, vulnerable, and dead cells in the target organ.
29. The non-transitory computer readable medium of claim 28, wherein simulating cellular necrosis in the target organ based on the simulated thermal diffusion further comprises:
at each time step, updating at least one tissue parameter of a biological thermal model used to simulate the thermal diffusion based on the concentrations of live, vulnerable, and dead cells in the target organ calculated at that time step before simulating the thermal diffusion at the next time step.
30. The non-transitory computer readable medium of claim 21, the operations further comprising:
generating a viewable view of a temperature map showing the simulated heat diffusion in the target organ.
Applications Claiming Priority (6)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201261724567P | 2012-11-09 | 2012-11-09 | |
| US61/724567 | 2012-11-09 | ||
| US61/724,567 | 2012-11-09 | ||
| US14/071,688 US9846765B2 (en) | 2012-11-09 | 2013-11-05 | System and method for patient specific modeling of liver tumor ablation |
| US14/071,688 | 2013-11-05 | ||
| US14/071688 | 2013-11-05 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103800075A CN103800075A (en) | 2014-05-21 |
| CN103800075B true CN103800075B (en) | 2018-02-23 |
Family
ID=50682550
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310757217.2A Active CN103800075B (en) | 2012-11-09 | 2013-11-08 | The system and method for the specific modeling of patient for hepatic tumor ablation art |
Country Status (2)
| Country | Link |
|---|---|
| US (1) | US9846765B2 (en) |
| CN (1) | CN103800075B (en) |
Families Citing this family (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105208957B (en) | 2013-02-26 | 2019-01-22 | 西门子公司 | The system and method for interactive patient particular dummy for radio frequency ablation therapy |
| US9259287B2 (en) * | 2013-04-02 | 2016-02-16 | Siemens Aktiengesellschaft | Patient specific planning and simulation of ablative procedures |
| US10402535B2 (en) | 2014-02-26 | 2019-09-03 | Siemens Healthcare Gmbh | System and method for personalized computation of tissue ablation extent based on medical images |
| WO2015153832A1 (en) | 2014-04-02 | 2015-10-08 | Siemens Aktiengesellschaft | System and method for characterization of electrical properties of the heart from medical images and body surface potentials |
| EP2933743B1 (en) * | 2014-04-18 | 2019-05-08 | Université de Rennes 1 | Method of characterizing molecular diffusion within a body from a set of diffusion-weighted magnetic resonance signals and apparatus for carrying out such a method |
| US9589379B2 (en) | 2014-06-24 | 2017-03-07 | Siemens Healthcare Gmbh | System and method for visualization of cardiac changes under various pacing conditions |
| US10354758B2 (en) | 2014-08-28 | 2019-07-16 | Siemens Healthcare Gmbh | System and method for patient-specific image-based simulation of atrial electrophysiology |
| CN107645927B (en) | 2015-02-17 | 2021-10-08 | 皇家飞利浦有限公司 | Apparatus and method for assisting tissue ablation |
| JP2018509231A (en) * | 2015-03-26 | 2018-04-05 | コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． | System and method for tumor ablation treatment planning including core tumor, margin, and range of healthy tissue |
| CN107847276B (en) * | 2015-04-02 | 2021-03-16 | 哈特弗罗公司 | System and method for providing personalized estimates of biological heat transfer |
| EP3345156B1 (en) * | 2015-09-02 | 2019-08-28 | Siemens Healthcare GmbH | Cfd simulation assisted 4d dsa reconstruction |
| US9619594B1 (en) * | 2015-11-25 | 2017-04-11 | International Business Machines Corporation | Configuration of large scale advection diffusion models with predetermined rules |
| TWI578261B (en) * | 2015-12-04 | 2017-04-11 | 財團法人金屬工業研究發展中心 | Pre-surgery planning system and pre-surgery planning method |
| CN105825070A (en) * | 2016-04-06 | 2016-08-03 | 南方医科大学南方医院 | Measurement method of virtual hepatic vein pressure gradient |
| US11664125B2 (en) | 2016-05-12 | 2023-05-30 | Siemens Healthcare Gmbh | System and method for deep learning based cardiac electrophysiology model personalization |
| EP3555778B1 (en) * | 2016-12-15 | 2020-10-21 | Sintef TTO AS | Method and process for providing a subject-specific computational model used for decision support and making diagnosis of cardiovascular diseases |
| US10402976B2 (en) * | 2017-05-16 | 2019-09-03 | Siemens Healthcare Gmbh | Isolation of aneurysm and parent vessel in volumetric image data |
| JP6756669B2 (en) * | 2017-06-12 | 2020-09-16 | 株式会社日立製作所 | Treatment support device and treatment support method |
| CN107578820A (en) * | 2017-08-21 | 2018-01-12 | 北京工业大学 | A kind of biological properties parameter characterization method based on single needle actual measurement |
| CN108830017B (en) * | 2018-07-05 | 2022-06-24 | 上海美杰医疗科技有限公司 | Radio frequency heating temperature field prediction system based on individual impedance |
| CN109157280A (en) * | 2018-08-10 | 2019-01-08 | 重庆大学 | Irreversible electroporated tissue ablation effect dynamic realtime assessment equipment |
| US10898151B2 (en) * | 2018-10-31 | 2021-01-26 | Medtronic Inc. | Real-time rendering and referencing for medical procedures |
| US11166645B2 (en) | 2018-12-18 | 2021-11-09 | Biosense Webster (Israel) Ltd. | Visualizing lesions formed by thermal ablation in a magnetic resonance imaging (MRI) scan |
| CN109793570B (en) * | 2019-04-02 | 2024-04-05 | 江苏省肿瘤医院 | Solid tumor cutting treatment device based on ablation |
| CN110246586A (en) * | 2019-05-28 | 2019-09-17 | 南京航空航天大学 | A kind of finite element model method for building up of the preoperative emulation of liver tumour microwave ablation |
| CN111110347B (en) * | 2019-11-29 | 2021-06-01 | 中奕智创医疗科技有限公司 | Ultrasonic positioning system, device and storage medium based on biplane image |
| CN113768484B (en) * | 2020-06-10 | 2022-06-24 | 上海美杰医疗科技有限公司 | Method and system for evaluating personalized blood perfusion rate of biological tissue |
| CN111743626B (en) * | 2020-07-03 | 2021-09-10 | 海杰亚（北京）医疗器械有限公司 | Tumor puncture path acquisition device and storage medium |
| CN112562859A (en) * | 2020-10-20 | 2021-03-26 | 应葵 | Intelligent simulation model training system and training method for tumor thermal ablation operation |
| CN113707272B (en) * | 2021-08-02 | 2024-02-02 | 复旦大学附属中山医院 | Model for evaluating suitable crowd for interventional therapy of radial artery access liver cancer and construction method |
| US11980492B2 (en) * | 2021-11-05 | 2024-05-14 | GE Precision Healthcare LLC | System and method for deep-learning based estimation of coronary artery pressure drop |
| CN114469342B (en) * | 2022-01-17 | 2023-07-25 | 四川大学华西医院 | Definition method, establishment system and application of tumor margin edge distance field |
| CN114451986A (en) * | 2022-01-19 | 2022-05-10 | 杭州堃博生物科技有限公司 | Steam ablation treatment method, device, system, equipment and medium |
| CN115082649B (en) * | 2022-08-16 | 2022-12-02 | 广东欧谱曼迪科技有限公司 | Liver segmentation method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101563041A (en) * | 2006-12-08 | 2009-10-21 | 皇家飞利浦电子股份有限公司 | A system, method, computer-readable medium, and use for planning combined therapy |
| CN101859341A (en) * | 2009-04-13 | 2010-10-13 | 盛林 | Image-guided ablation surgery planning device |
| US20110196385A1 (en) * | 2010-02-09 | 2011-08-11 | Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung E.V. | Ablated object region determining apparatuses and methods |
| CN102264315A (en) * | 2008-12-23 | 2011-11-30 | 克莱米迪克斯有限责任公司 | Isotherm-based tissue ablation control system and method |
| CN102346811A (en) * | 2010-07-21 | 2012-02-08 | 西门子公司 | Method and system for comprehensive patient-specific modeling of the heart |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101316560B (en) | 2005-12-02 | 2011-01-26 | 皇家飞利浦电子股份有限公司 | Automating the ablation procedure to minimize the need for manual intervention |
| US8267927B2 (en) | 2007-01-24 | 2012-09-18 | Koninklijke Philips Electronics N.V. | Advanced ablation planning |
| DE102007053394B4 (en) | 2007-11-09 | 2014-04-03 | Siemens Aktiengesellschaft | Method and device for planning and / or reviewing interventional radiofrequency thermal ablation |
| US10431001B2 (en) | 2007-11-21 | 2019-10-01 | Edda Technology, Inc. | Method and system for interactive percutaneous pre-operation surgical planning |
| US20090221999A1 (en) | 2008-02-29 | 2009-09-03 | Ramin Shahidi | Thermal Ablation Design and Planning Methods |
| JP5685546B2 (en) | 2008-12-03 | 2015-03-18 | コーニンクレッカ フィリップス エヌ ヴェ | A feedback system that integrates interventional planning and navigation |
| WO2013049108A2 (en) | 2011-09-26 | 2013-04-04 | Profound Medical Inc. | System and method for control and monitoring of conformal thermal therapy |
| US9277970B2 (en) | 2012-07-19 | 2016-03-08 | Siemens Aktiengesellschaft | System and method for patient specific planning and guidance of ablative procedures for cardiac arrhythmias |
-
2013
- 2013-11-05 US US14/071,688 patent/US9846765B2/en active Active
- 2013-11-08 CN CN201310757217.2A patent/CN103800075B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101563041A (en) * | 2006-12-08 | 2009-10-21 | 皇家飞利浦电子股份有限公司 | A system, method, computer-readable medium, and use for planning combined therapy |
| CN102264315A (en) * | 2008-12-23 | 2011-11-30 | 克莱米迪克斯有限责任公司 | Isotherm-based tissue ablation control system and method |
| CN101859341A (en) * | 2009-04-13 | 2010-10-13 | 盛林 | Image-guided ablation surgery planning device |
| US20110196385A1 (en) * | 2010-02-09 | 2011-08-11 | Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung E.V. | Ablated object region determining apparatuses and methods |
| CN102346811A (en) * | 2010-07-21 | 2012-02-08 | 西门子公司 | Method and system for comprehensive patient-specific modeling of the heart |
Non-Patent Citations (2)
| Title |
|---|
| A Three-State Mathematical Model of Hyperthermic Cell Death;O’Neill等;《Annals of Biomedical Engineering》;20110131;第39卷(第1期);摘要第572页第2栏到第575页及附图1-2 * |
| A two equation coupled system for determination of liver tissue temperature during thermal ablation;PENG等;《International Journal of Heat and Mass Transfer》;20110430;第54卷(第9-10期);摘要及第2100页到第2102页、第2106页到第2107页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103800075A (en) | 2014-05-21 |
| US9846765B2 (en) | 2017-12-19 |
| US20140136174A1 (en) | 2014-05-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103800075B (en) | The system and method for the specific modeling of patient for hepatic tumor ablation art | |
| US10748438B2 (en) | System and method for interactive patient specific simulation of radiofrequency ablation therapy | |
| CN104856755B (en) | The system and method that individuation for the ablation of tissue based on medical image calculates | |
| US9259287B2 (en) | Patient specific planning and simulation of ablative procedures | |
| Audigier et al. | Efficient lattice boltzmann solver for patient-specific radiofrequency ablation of hepatic tumors | |
| Kok et al. | Current state of the art of regional hyperthermia treatment planning: a review | |
| US11908584B2 (en) | Methods and systems for modeling a necrotized tissue volume in an ablation procedure | |
| Chen et al. | Optimizing electrode placement using finite-element models in radiofrequency ablation treatment planning | |
| Payne et al. | Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours | |
| Singh et al. | Pre-operative assessment of ablation margins for variable blood perfusion metrics in a magnetic resonance imaging based complex breast tumour anatomy: Simulation paradigms in thermal therapies | |
| Chua | Fundamental experiments and numerical investigation of cryo-freezing incorporating vascular network with enhanced nano-freezing | |
| Audigier et al. | Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images | |
| US20200179051A1 (en) | Therapeutic guidance compute node controller | |
| Iljaž et al. | Subdomain BEM formulations for the solution of bio-heat problems in biological tissue with melanoma lesions | |
| US20240058062A1 (en) | System and method for ablation treatment of tissue with interactive guidance | |
| He et al. | An efficient thermal evolution model for cryoablation with arbitrary multi-cryoprobe configuration | |
| Tanaka et al. | Cryosurgery planning using bubble packing in 3D | |
| Thaokar et al. | Temperature field reconstruction for minimally invasive cryosurgery with application to wireless implantable temperature sensors and/or medical imaging | |
| Audigier et al. | Parameter estimation for personalization of liver tumor radiofrequency ablation | |
| Kath et al. | Robust GPU-based virtual reality simulation of radio-frequency ablations for various needle geometries and locations | |
| Rossmann et al. | Platform for patient-specific finite-element modeling and application for radiofrequency ablation | |
| Talbot et al. | Interactive planning of cryotherapy using physics-based simulation | |
| Kerbl et al. | Intervention planning of hepatocellular carcinoma radio-frequency ablations | |
| Golkar et al. | Fast GPU computation of 3D isothermal volumes in the vicinity of major blood vessels for multiprobe cryoablation simulation | |
| Wang et al. | Thermo-mechanical coupling simulation analysis of cryotherapy on real anatomical structure lung cancer model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220125 Address after: Erlangen Patentee after: Siemens Healthineers AG Address before: Munich, Germany Patentee before: SIEMENS AG |